Upload app.py

app.py

@@ -1,121 +1,4 @@
 
-# import gradio as gr
-
-# # Use a pipeline as a high-level helper
-# from transformers import pipeline
-
-# # Use a pipeline as a high-level helper
-# # Load model directly
-# from transformers import AutoImageProcessor, AutoModelForImageClassification
-
-# # processor = AutoImageProcessor.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-# # model = AutoModelForImageClassification.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-# pipe = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-
-
-# # $ pip install gradio_client fastapi uvicorn
-
-# import requests
-# from PIL import Image
-# from transformers import pipeline
-# import io
-# import base64
-
-# Initialize the pipeline
-# pipe = pipeline('image-classification')
-
-# def load_image_from_path(image_path):
-#     return Image.open(image_path)
-
-# def load_image_from_url(image_url):
-#     response = requests.get(image_url)
-#     return Image.open(io.BytesIO(response.content))
-
-# def load_image_from_base64(base64_string):
-#     image_data = base64.b64decode(base64_string)
-#     return Image.open(io.BytesIO(image_data))
-
-# def predict(image_input):
-#     if isinstance(image_input, str):
-#         if image_input.startswith('http'):
-#             image = load_image_from_url(image_input)
-#         elif image_input.startswith('/'):
-#             image = load_image_from_path(image_input)
-#         else:
-#             image = load_image_from_base64(image_input)
-#     elif isinstance(image_input, Image.Image):
-#         image = image_input
-#     else:
-#         raise ValueError("Incorrect format used for image. Should be an URL linking to an image, a base64 string, a local path, or a PIL image.")
-    
-#     return pipe(image)
-
-
-# def predict(image):
-#   return pipe(image)
-
-# def main():
-#     # image_input = 'path_or_url_or_base64'  # Update with actual input
-#     # output = predict(image_input)
-#     # print(output)
-        
-    # demo = gr.Interface(
-    #   fn=predict,
-    #   inputs='image',
-    #   outputs='text',
-    # )
-
-    # demo.launch()
-
-# import requests
-# import torch
-# from PIL import Image
-# from torchvision import transforms
-
-# def predict(inp):
-#     inp = Image.fromarray(inp.astype("uint8"), "RGB")
-#     inp = transforms.ToTensor()(inp).unsqueeze(0)
-#     with torch.no_grad():
-#         prediction = torch.nn.functional.softmax(model(inp.to(device))[0], dim=0)
-#     return {labels[i]: float(prediction[i]) for i in range(1000)}
-
-
-# inputs = gr.Image()
-# outputs = gr.Label(num_top_classes=2)
-
-# io = gr.Interface(
-#     fn=predict, inputs=inputs, outputs=outputs, examples=["dog.jpg"]
-# )
-# io.launch(inline=False, share=True)
-
-
-
-
-
-
-
-# import gradio as gr
-# from transformers import pipeline
-
-# pipeline = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-
-# def predict(image):
-#   predictions = pipeline(image)
-#   return {p["label"]: p["score"] for p in predictions}
-
-# gr.Interface(
-#     predict,
-#     inputs=gr.inputs.Image(label="Upload Image", type="filepath"),
-#     outputs=gr.outputs.Label(num_top_classes=2),
-#     title="AI Generated? Or Not?",
-#     allow_flagging="manual"
-# ).launch()
-
-    
-
-# if __name__ == "__main__":
-#     main()
-
 import gradio as gr
 from transformers import pipeline
 
@@ -127,9 +10,9 @@ def predict(input_img):
 
 gradio_app = gr.Interface(
     predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    inputs=gr.Image(label="Select Image", sources=['upload', 'webcam'], type="pil"),
     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+    title="AI Generated? Or Not?",
 )
 
 if __name__ == "__main__":